Chapter 4 – The Most Common Data Cleaning Operations

1. B – To enhance data accuracy in the analysis – Removing duplicates is crucial to prevent inaccuracies in data analysis, especially when dealing with numerical values.
2. C – Product Name, as the main identifier – In the provided example, the Product Name column is selected to remove duplicates, as it serves as the main identifier.
3. B – Distorts analysis results – Missing data, or NULL values, can distort analysis results and visuals.
4. C – To gain desired dimensions for analysis – for example, splitting a date field – Columns may need to be split to extract specific dimensions for analysis.
5. C – Split Columns by Delimiter, based on data format – In the Date table example, the By Delimiter function is used to split the date column based on the / delimiter.
6. C – Merging columns to format date data – Merging columns may...